Next Article in Journal
Image Error Concealment Based on Deep Neural Network
Previous Article in Journal
An Improved Squirrel Search Algorithm for Global Function Optimization
Open AccessArticle

Direct Superbubble Detection

by 1,2,* and 1,2,3,4,5,6,7
1
Competence Center for Scalable Data Services and Solutions Dresden/Leipzig, Universität Leipzig, Augustusplatz 12, D-04107 Leipzig, Germany
2
Bioinformatics Group, Department of Computer Science, Universität Leipzig, Härtelstraße 16–18, D-04107 Leipzig, Germany
3
Interdisciplinary Center for Bioinformatics, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and Leipzig Research Center for Civilization Diseases, University Leipzig, D-04107 Leipzig, Germany
4
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany
5
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria
6
Facultad de Ciencias, Universidad National de Colombia, Sede Bogotá, Colombia
7
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(4), 81; https://doi.org/10.3390/a12040081
Received: 7 March 2019 / Revised: 10 April 2019 / Accepted: 12 April 2019 / Published: 17 April 2019
Superbubbles are a class of induced subgraphs in digraphs that play an essential role in assembly algorithms for high-throughput sequencing data. They are connected with the remainder of the host digraph by a single entrance and a single exit vertex. Linear-time algorithms for the enumeration superbubbles recently have become available. Current approaches require the decomposition of the input digraph into strongly-connected components, which are then analyzed separately. In principle, a single depth-first search could be used, provided one can guarantee that the root of the depth-first search (DFS)-tree is not itself located in the interior or the exit point of a superbubble. Here, we describe a linear-time algorithm to determine suitable roots for a DFS-forest that is guaranteed to identify the superbubbles in a digraph correctly. In addition to the advantages of a more straightforward implementation, we observe a nearly three-fold gain in performance on real-world datasets. We present a reference implementation of the new algorithm that accepts many commonly-used input formats for digraphs. It is available as open source from github. View Full-Text
Keywords: superbubble; depth-first search; cycles; linear time algorithm superbubble; depth-first search; cycles; linear time algorithm
Show Figures

Figure 1

MDPI and ACS Style

Gärtner, F.; Stadler, P.F. Direct Superbubble Detection. Algorithms 2019, 12, 81.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop